Short-Term Residential Load Forecasting Based on the Fusion of Customer Load Uncertainty Feature Extraction and Meteorological Factors

Abstract

With the proliferation of distributed energy resources, advanced metering infrastructure, and advanced communication technologies, the grid is transforming into a flexible, intelligent, and collaborative system. Short-term electric load forecasting for individual residential customers is playing an increasingly important role in the operation and planning of the future grid. Predicting the electrical load of individual households is more challenging with higher uncertainty and volatility at the household level compared to the total electrical load at the feeder and regional levels. The previous research results show that the accuracy of forecasting using machine learning and a single deep learning model is far from adequate and there is still room for improvement.